Probability
Exact temporal variations and parameter estimation for space–time fractional stochastic heat equations driven by multiplicative noise
Yongkang Li, Yaozhong Hu and Litan Yan
We establish exact limits for the normalized temporal power variations of every fixed real order \(\gamma\geq2\) for the mild solution to a one-dimensional space–time fractional
stochastic heat equation driven by multiplicative space–time white noise. The results include the exact normalized quadratic variation and the critical power variation of order \(2\alpha/(\alpha-\beta)\) as special cases. We also obtain the corresponding spatially averaged temporal variations. As applications, we construct consistent
estimators for the drift parameter and for the ratio \(\beta/\alpha\).
Read the full abstract ▾
Dynamical Systems
Uniqueness of convex central configurations in the planar (1 + 4)-body problem
Kaitai Xiao
We study strictly convex central configurations of the planar Newtonian five-body problem with one dominant mass and four satellites of arbitrary positive masses. We prove that a single positive bound on the total satellite-to-primary mass ratio guarantees existence and uniqueness for every prescribed directed order of the five bodies on the convex hull, up to translations, rotations and positive scaling. The bound is independent of all satellite mass ratios, including ratios arbitrarily close to the boundary of the mass simplex. The constrained Hessian is nondegenerate on the six-dimensional shape space. We first prove uniqueness and nondegeneracy for the convex coorbital limit by an angular Hessian estimate. We then classify the possible collision limits as the satellite mass ratios vary and construct regularized local continuations for pairs, triples and pairs within triples. An exact factorization of the signed areas preserves strict convexity on independent parameter neighborhoods. These continuations connect every sufficiently small-mass configuration to the unique coorbital configuration and yield the common bound.
Read the full abstract ▾
Quantum Physics
Geometry-Controlled Squeezed-Waveguide Enabled Quantum Batteries
Luis D. Zambrano-Palma, Yusef Maleki and M. Suhail Zubairy
Harnessed nonclassical resources to enhance the performance of
quantum batteries is an important goal of
quantum technology. In this work, we investigate multi-emitter batteries coupled to a one-dimensional waveguide driven by a broadband squeezed-
vacuum reservoir, and determine how the waveguide geometry controls the battery performance and and their thermodynamic features. We show that for a single emitter, the squeezed reservoir deposits finite energy while the state remains passive, yielding zero ergotropy. For two emitters, however, phase-sensitive reservoir correlations generate coherence and can drive the steady state into a fully nonpassive regime. For three emitters, collective thermal dynamics already supports finite ergotropy, while squeezing further increases the extractable component of the stored energy. Crucially, because the collective and phase-sensitive couplings depend on the emitter positions, geometry provides direct control over the nonclassical correlations and, consequently, over the nonpassivity of the steady state. The ergotropy can therefore be strongly modulated while the stored energy changes only weakly, and in the two-emitter configuration geometry alone can switch the battery between a passive steady state and one in which all stored energy is unitarily extractable.
Read the full abstract ▾
Artificial Intelligence
Recursive Improvement of a Differentiable Scientific Software Ecosystem
Pengcheng Hou, Xiaojun Tan, Sihan Hu et al. (+5 more)
Differentiable programming connects scientific computation with gradient-based inference, learning and design. Extending these capabilities across a heterogeneous software ecosystem requires specialized effort to implement derivatives, integrate interfaces and evaluate quality. AI coding
agents can accelerate this transformation, but translating their capabilities into useful scientific software requires identifying research needs and evaluating how well implementations meet them. We present an environment for
agent-driven
evolution of differentiable scientific software that connects demand identification, development and quality evaluation. A unified differentiation interface exposes reusable derivative rules alongside existing numerical routines, allowing research tasks to share these capabilities. Research requirements guide development, with implementations assessed through independent derivative checks, workflow tests and performance evaluation. Validated software, research programs and tests become shared resources for subsequent studies. We construct and validate automatic differentiation extensions across 20 packages spanning physical, chemical and biological modeling, with research workflows demonstrating reuse across tasks.
Benchmarks demonstrate computational savings over finite differences in gradient evaluation and complete parameter estimation. Research-driven revisions make previously unsupported workflows differentiable, correct derivatives of scientific observables and eliminate redundant computation.
Quantum-control and thermal-design studies revise objectives in response to physical evaluation, improving designs while reusing existing derivatives. This work provides a practical approach to expanding differentiable programming across established scientific software and organizing AI
agents around the recursive improvement of a shared computational ecosystem.
Read the full abstract ▾
All authors Pengcheng Hou, Xiaojun Tan, Sihan Hu, Ruisi Wang, Shuo Chen, Lei Wang, Youjin Deng, Kun Chen
Quantum Physics
Quantum Submodular Maximization
Yonggang Jiang, Xiaoming Sun, Penghui Yao et al. (+3 more)
We study the
quantum query complexity of maximizing a non-negative submodular function, considering both the unconstrained setting and, for monotone functions, a cardinality constraint $k$ on an $n$-element ground set. In the exact reversible digital value-oracle model, our unconstrained
algorithm achieves an expected $(1/2-\varepsilon)$-approximation using only $O_\varepsilon(\log n)$ queries. In contrast, any classical randomized
algorithm that attains a fixed expected ratio above $1/4$ requires $\Omega(n/\log n)$ queries (Li, Feldman, Kazemi, and Karbasi, 2022), establishing an exponential separation in query complexity. For cardinality-constrained maximization, we give a bounded-error
quantum algorithm that achieves a $(1-1/e-\varepsilon)$-approximation using $\widetilde O_\varepsilon(\min\{\sqrt n,n/k\})$ queries. When $k=o(n)$, our
algorithm achieves at least a quadratic speedup up to logarithmic factors over classical randomized
algorithms (Mirzasoleiman, Badanidiyuru, Karbasi, Vondrák, and Krause, 2015; Peng and Rubinstein, 2025). Moreover, when $k=cn$ for any fixed rational $c<1-1/e-\varepsilon$, the query complexity reduces to $O_{\varepsilon,c}(\log n)$, yielding an exponential separation from the classical $\Omega(n/\log n)$ lower bound (Li, Feldman, Kazemi, and Karbasi, 2022). We further prove
quantum lower bounds of $\exp(\Omega(\varepsilon^2n))$ queries for achieving a ratio beyond $1/2+\varepsilon$ without constraints, and $\exp(\Omega(\varepsilon^2k))$ queries for exceeding $1-1/e+\varepsilon$ when $k/n\le\varepsilon$. These barriers demonstrate that
quantum computation offers no exponential speedup at these approximation thresholds.
Read the full abstract ▾
All authors Yonggang Jiang, Xiaoming Sun, Penghui Yao, Zekun Ye, Jialin Zhang, Zhijie Zhang
Machine Learning
Coupling Noisy Pairwise Knowledge to the DAG Posterior for Causal Discovery
Guoliang Xu and James E Corter
External causal reports can improve structure learning from limited observations, but their reliability varies across sources and variable pairs. We introduce HB-NoisyKG, a
Bayesian framework that combines observational data with repeated causal reports from sources such as large
language models. Each report is a noisy observation of a direct pair state implied by one DAG. A feature-conditioned Beta prior pools information about pair reliability, and a shared error
matrix captures systematic mistakes. Alternating inference uses the graph posterior to refine reliability estimates, which determine how reports influence subsequent graph updates. The report likelihood uses only graph pair-state marginals, so the same observation layer supports discrete and continuous likelihoods in graph-only and joint inference. Against an 80-restart no-KG baseline, HB uses at most 80 total restarts and lowers mean SHD from 22.39 to 16.06 on five discrete
benchmarks. On a physical light tunnel with random variable IDs and retained descriptions, HB lowers SHD from 39.00 for no-KG to 27.30. On continuous Sachs, graph-only BGe raises AUROC by 0.121 over no-KG Top-K. In a controlled synthetic study, continued updating also reduces mean reliability estimation error and held-out report log loss compared with one-time estimation.
Read the full abstract ▾
Atmospheric and Oceanic Physics
ClimateBench v2.0: Probabilistic Climate Model Benchmarking
Duncan Watson-Parris, Willa Tobin, Aytaç Paçal et al. (+32 more)
We present ClimateBench v2, a standardized protocol for evaluating
climate models on diagnostics expected to be informative for their skill in projecting mid-century regional temperature and precipitation changes. The protocol is designed to evaluate any physics-based, data-driven, or hybrid
climate model on equal footing using a common set of observational and out-of-distribution tests. We define three tiers of evaluation. Tier I establishes physical credibility through entry-ticket tests of energy conservation, coupled (co-)variability, and basic forced responses. Tier II scores models against post-2015 observations of surface temperature, precipitation, radiative fluxes, sea ice, and key modes of variability using fair CRPS as the primary probabilistic score, complemented by distributional and ensemble-consistency diagnostics. Tier III tests out-of-distribution generalization through paleoclimate simulations spanning the Last Interglacial, Last Glacial Maximum, and Mid-Holocene, and through perfect-model experiments in which data-driven models must predict the future
climate of existing Earth system models from historical data alone. We reserve all observational data after 2015 for testing, and submissions must include multiple ensemble members to enable probabilistic evaluation. This reservation exploits a new opportunity provided by the decade of observations accumulated since the end of the CMIP6 historical experiment, which constitutes an out-of-sample record of forced
climate change (and internal variability) for the current generation of models, and we quantify, in an idealized setting, the information it carries about mid-century warming. We provide the evaluation code, observational reference
datasets, and perfect-model training data as an open
benchmark to drive measurable progress in
climate projection across all modeling approaches.
Read the full abstract ▾
All authors Duncan Watson-Parris, Willa Tobin, Aytaç Paçal, Manuel Schlund, V. Balaji, Kevin Bowman, Chris Bretherton, Peter M. Caldwell, Will Chapman, William D. Collins, Gregory S. Elsaesser, Pierre Gentine, Helene Hewitt, Stephan Hoyer, Ralph Keeling, Nikolay Koldunov, David M. Lawrence, Christian Lessig, Daniel J. Lunt, J. David Neelin, Mike Pritchard, Sarah Purkey, Gavin Schmidt, Tapio Schneider, Michael Schulz, Tiffany Shaw, Isla R. Simpson, Graeme Stephens, Aneesh C. Subramanian, Joao Teixeira, Jessica Tierney, Andrew I. L. Williams, Laure Zanna, Veronika Eyring, Rose Yu
High Energy Physics (Phenomenology)
Violation and Restoration of Probability Positivity in Perturbative Quantum Field Theory: From Quantum Mechanics to Quantum Chromodynamics
Bin Gong, Xu-Dong Huang, Jian-Xiong Wang et al. (+1 more)
In
quantum mechanics the modulus square of the wave function is the probability of an event — positivity is self-evident.
Quantum field theory corresponds to
quantum mechanics one-to-one: the wave function corresponds to the
matrix element, so the probability of a process should be the modulus square of the
matrix element, positive by construction. In the founding era of perturbative
quantum field theory, however, Feynman, Schwinger and Tomonaga treated inclusive processes in
quantum electrodynamics, where the truncation of the squared amplitude was forced by infrared cancellations and rendered harmless by the small coupling $\alpha\simeq1/137$. Carried over to exclusive processes in
quantum chromodynamics, where the coupling is large, this practice produced negative decay and production rates already at NLO, persisting at NNLO. We point out that for exclusive processes, whose initial and final states are colorless for QCD and electrically neutral for QED, the infrared divergences cancel amplitude by amplitude and the KLN constraint is absent, so the complete modulus square of the truncated amplitude is the proper, manifestly positive definition of the probability. We formulate amplitude-level NRQCD factorization as the systematic prescription that restores positivity, show that every amplitude-level truncated observable admits the effective representation $\Gamma^{\mathrm{amp}}=\Gamma_{\mathrm{LO}}\bigl|1+\frac{\alpha_s}{\pi}(c_1+ic_2)+\frac{\alpha_s^2}{\pi^2}(c_3+ic_4)\bigr|^2$, and demonstrate, with the complete NNLO QCD corrections to $J/\psi\to3\gamma$, $\Upsilon\to3\gamma$, $e^+e^-\to J/\psi+J/\psi$ and the eight exclusive charmonium production channels at $B$ factories, that the prescription yields positive and better-converging predictions.
Read the full abstract ▾
All authors Bin Gong, Xu-Dong Huang, Jian-Xiong Wang, Chao Zeng
Quantum Physics
The collapse time of weak measurement on $N$ entangled sites
Truong-Son P. Van and David W. Snoke
We determine the time required for local weak measurements to collapse to one occupied site in an initial superposition of $N$ alternatives in the model of PRA 111, 032217 (2025). For equal initial occupation probabilities and a fixed confidence threshold $1-\delta$, we prove $T_N/\ln N\to1/2$ in $L^1$, with time measured in units of the characteristic measurement time $\tau_m$ of each detector. We also connect the asymptotic stabilization
theorem of that work to a finite measurement: the first site to reach $1-\delta$ is the eventual outcome with conditional probability exactly $1-\delta$. The result corrects the previously proposed double-logarithmic asymptotic scaling. The finite-range numerical fit does not determine a constant asymptotic correction or a precise finite-$N$ law.
Read the full abstract ▾
Artificial Intelligence
$\mathrm{TRIZ}^{a}$: Guiding Agent Evolution from Pattern Recognition to Solution Invention
Wenyin Liu, Yiheng Huang and Kai Wang
We propose $\mathrm{TRIZ}^{a}$ (TRIZ exponentiated by an
agent), a general R&D automation paradigm that combines TRIZ inventive theory with LLM-driven
agent evolutionary search. TRIZ's 40 inventive principles and contradiction
matrix provide structured, explainable directions for solution generation, replacing random or untyped mutation with theory-guided ideation. Functional information (FI), operationalized under a frozen reference contract, is combined with TRIZ Ideality to measure useful and harmful function on a commensurable information scale, while hard gates keep promotion distinct from metric improvement. We validate $\mathrm{TRIZ}^{a}$ in cybersecurity–an
adversarial and rapidly evolving domain–on PowerDuck GOOSE, CICIoT2023, and CIC-DDoS2019. Under paired-rerun protocols with protocol fingerprinting and hard-gate validation, the legacy experiments yield absolute F1 improvements of $+2.88$, $+4.23$, and $+0.15$ percentage points, respectively. A completed 45-activity CICIoT2023 campaign further increases macro-F1 from $0.8325$ to $0.8483$, but does not pass its frozen promotion gate. Every result remains traceable from contradiction identification and TRIZ principle selection to code transformation, evaluation metrics, and promotion decision.
Read the full abstract ▾
Computer Vision
EagleDepth: Efficient Fine-Grained Depth Estimation via Pixel Diffusion Decoder
Bowen Chai, Tianbao Zhang, Shuyu Wu et al. (+3 more)
Recovering detailed geometry from high-resolution images is critical for precise perception of the surroundings and objects. However, existing methods which use latent-space modeling and VAE reconstruction can compromise
geometric details. Furthermore, decoding from latent codes introduces substantial inference overhead. To address those issues, we present EagleDepth, an efficient framework for high-resolution
monocular depth estimation that combines the
geometric priors of latent
diffusion with fine-grained pixel-space generation. Our key idea is to retain depth-aware latent representations as guidance while generating the final depth map directly in pixel space. We train the latent and pixel components sequentially: first, we fine-tune a pretrained latent
diffusion model using paired RGB–depth supervision; then, we adapt a pretrained pixel
diffusion decoder, PiD, to predict depth conditioned on the learned features. Training of the pixel component starts at 1024 resolution and continues across multiple resolutions up to 4K. The latent branch processes resized, lower-resolution RGB images, while the pixel branch generates depth at the target resolution, bypassing the original VAE decoder. This design preserves learned
geometric knowledge without requiring the latent backbone to operate at the output resolution. On five commonly used depth estimation
datasets and the high-resolution Synth4K
dataset, our framework achieves state-of-the-art depth estimation performance, with faster inference and better preservation of fine structures and object boundaries.
Read the full abstract ▾
All authors Bowen Chai, Tianbao Zhang, Shuyu Wu, Dexin Zuo, Zhaoxin Fan, Danping Zou
Solar and Stellar Astrophysics
Cross-Modal Solar Image Synthesis: Adapting the Surya Foundation Model from He I 10830 Å to EUV Translation and Coronal Hole Segmentation
Marco Marena, Andrés Muñoz Jaramillo, Qin Li et al. (+8 more)
The long observational record of He I 10830 Å offers a means to investigate
solar morphology before modern extreme-ultraviolet (EUV) imaging. We adapt the Surya
solar foundation model to predict
Solar Dynamics Observatory/Atmospheric Imaging Assembly (SDO/AIA) 94, 193, and 304 Å images and a coronal hole (CH) probability map from full-disk helium observations. A convolutional input adapter, low-rank backbone updates, and dedicated output decoders learn from temporally paired, geometrically registered observations, with Spatial Possibilistic Clustering
Algorithm (SPOCA) catalog polygons providing CH supervision. On observations held out from downstream fine-tuning, the selected dedicated models achieve disk-restricted correlation coefficients (CCs) of 0.8196, 0.8885, and 0.8398 for the three AIA channels, respectively; the CH model achieves an intersection over union (IoU) of 0.4360. The predictions recover broad
solar structure, although local agreement varies substantially by channel. An optional residual refiner addresses spatial detail, and its application on pre-SDO dates improves the correlation of synthetic AIA 304 with
Solar and Heliospheric Observatory/Extreme-ultraviolet Imaging
Telescope (SOHO/EIT) 304 references from 0.6511 to 0.7085. Together, these results support the feasibility of helium-conditioned EUV morphological proxies and motivate their use in historical reconstruction, within the scope of the downstream test and cross-instrument evaluation.
Read the full abstract ▾
All authors Marco Marena, Andrés Muñoz Jaramillo, Qin Li, Haodi Jiang, Jinghao Cao, Wen He, Ziyang Zhang, Chenxi Yuan, Chao Wang, Haimin Wang, Bo Shen
Robotics
Online Target-less Radar-LiDAR-Camera Extrinsic Calibration via Joint Optimization
Gunhee Shin, Yunsoo Kim, Chanhyuk Lee et al. (+7 more)
Fusing radar,
LiDAR, and camera enables robust perception in diverse and adverse conditions, but the fusion performance critically depends on accurate extrinsic calibration among the three sensors. In this paper, we address the problem of online target-less extrinsic calibration for the radar-
LiDAR-camera system. Existing target-less methods are mostly designed for a single sensor pair, and composing the pairwise results does not guarantee consistency across the three sensors. Moreover, the sparse and noisy radar measurements make the radar-involving pairs unreliable. To tackle these challenges, we propose a joint calibration framework that constructs residuals for each sensor pair and optimizes the extrinsics of all pairs together to minimize the overall residual. Furthermore, we introduce an adaptive radar noise filter that rejects spurious radar returns using a range-dependent margin, and a correspondence accumulation strategy that aggregates sparse radar correspondences over frames. We validate our method on an in-house radar-
LiDAR-camera
dataset covering diverse urban environments, where it reduces calibration errors across all sensor pairs over a state-of-the-art camera-
LiDAR baseline.
Read the full abstract ▾
All authors Gunhee Shin, Yunsoo Kim, Chanhyuk Lee, Wanhee Kim, Minwoo Lee, Sungwoo Han, Jeongwoo Woo, Hyuntai Chin, Minha Park, Hyun Myung
Robotics
NM-LIO: Multiple LiDAR-Inertial Odometry Addressing LiDAR Measurement Noise Discrepancy
Gunhee Shin, Seungjae Lee, Minho Oh et al. (+4 more)
Multiple
LiDAR-inertial odometry methods have been widely applied in robotic applications owing to their enhanced accuracy and reliability. However, adopting multiple
LiDAR systems can be challenging due to the discrepancies in measurement noise across different
LiDARs. Existing methods have typically overlooked the noise discrepancies, which can significantly affect accuracy. In this paper, we propose Noise-aware Multiple
LiDAR-Inertial Odometry (NM-LIO) that addresses the noise discrepancies. We integrate a noise model to quantify the measurement noise of each
LiDAR. Additionally, we estimate the uncertainty of the residuals based on the measurement noise, allowing the measurement model to capture the noise discrepancies. Our proposed method is evaluated on a public multiple
LiDAR dataset and compared with state-of-the-art methods. The experimental results demonstrate that the proposed method can accurately estimate the odometry in various environments by accounting for the noise discrepancies.
Read the full abstract ▾
All authors Gunhee Shin, Seungjae Lee, Minho Oh, Dongkyu Lee, Jaeyoung Lee, Youngwoo Seo, Hyun Myung
Analysis of PDEs
Global and nonglobal solutions for an abstract semilinear heat equation
Ricardo Castillo, Ricardo Freire and Miguel Loayza
In this paper, we focus on the existence of both global and non-global nonnegative solutions for the abstract semilinear heat equation $u_t - Lu = \sum_{i=1}^k \ell_i(t) f_i(u)$ in $L^p(X, \mu)$. Here $(X,\mu)$ represents a $\sigma$-finite measure space, $L$ is the infinitesimal generator of a sub-Markovian strongly continuous
semigroup of bounded linear operators in $L^p(X,\mu)$, and for each $i = 1, \ldots, k$, the functions $\ell_i: [0, \infty) \rightarrow [0, \infty)$ and $f_i: [0, \infty) \rightarrow [0, \infty)$ are continuous. We utilize our findings to apply to the
Laplacian operator on sub-Riemannian
manifolds and locally finite graphs, obtaining Fujita-type results. Our results extend the recent work of Lenz, Schmidt, and Zimmermann, "Blow-up of nonnegative solutions of an abstract semilinear heat equation" (Calc. Var. Partial Differential Equations, 2023).
Read the full abstract ▾
Symbolic Computation
A Finite Certificate for the Positive $n=11$ Vasc Inequality
Tiancheng Xu and Wensheng Yu
We establish the Vasc cyclic inequality for all strictly positive real eleven-tuples by a finite exact certificate. Cyclic rotation places a minimum at the first position, and rank words together with cumulative gaps reduce the problem to $10!=3{,}628{,}800$ homogeneous integer
polynomials of degree eleven. Independent coefficient reconstruction proves $3{,}358{,}617$ roots directly. The remaining $270{,}183$ roots are covered by $267{,}952$ ordinary midpoint certificates, $2{,}195$ complete binary subdivision certificates, and $36$ complete certificates containing partial sorting nodes. Each nontrivial leaf is checked by subtracting weighted AM-GM midpoint
circuits from a positive multiple of the transformed
polynomial and verifying every residual coefficient. Explicit word-set comparisons certify disjointness and exact coverage. We prove the soundness of the leaves and both subdivision rules, then recover the rational inequality by positive denominator clearing and continuity. The resulting proof combines explicit mathematical reductions with independently checked exact integer certificates.
Read the full abstract ▾
Operator Algebras
Cartan inverse semigroups and twisted Steinberg algebras
Nathan Brownlowe, Lisa Orloff Clark, Ying-Fen Lin et al. (+1 more)
We introduce the notion of a Cartan inverse
semigroup and an (algebraic) semi-Cartan subalgebra in an $R$-
algebra A, providing a purely algebraic analogue of Cartan
semigroups and semi-Cartan subalgebras in a C*-
algebra. This extends the theory of quasi-Cartan subalgebras, shifting the focus from designated subalgebras to designated subsemigroups. Our main result characterises twisted Steinberg
algebras in terms of the existence of a Cartan inverse
semigroup: we show that an $R$-
algebra admits a Cartan inverse
semigroup if and only if it is isomorphic to a twisted Steinberg
algebra. Moreover, when our construction is applied to a twisted Steinberg
algebra over an ample Hausdorff groupoid, the constructed twist is naturally isomorphic to the original one.
Read the full abstract ▾
All authors Nathan Brownlowe, Lisa Orloff Clark, Ying-Fen Lin, Lynnel D. Naingue
Machine Learning
TMAML: Temporal Model-Agnostic Meta-Learning for Cold-Start Time Series Forecasting
Wannes Janssens, Matthias Bogaert and Dirk Van den Poel
Cold-start
forecasting, the task of
forecasting a time series with little to no historical data, is a common challenge. Addressing it requires approaches that learn quickly from few datapoints and leverage information from related series, typically through static
covariates, to generalize well to unseen series. While some global
forecasting models can generate cold-start predictions by leveraging information shared across multiple series, they are not optimized for out-of-train-set generalization or adaptation from short histories. In this work, we formulate cold-start
forecasting as a few-window learning problem and introduce Temporal Model-Agnostic Meta-Learning (TMAML), which tailors the model-agnostic meta-learning
algorithm, originally developed for few-shot adaptation of neural networks, to deep time series
forecasting. TMAML constructs meta-tasks as temporally consistent support-query windows and pairs them with a temporal meta-training and meta-testing procedure, yielding
forecasting models that are explicitly optimized for cold-start
forecasting. We instantiate TMAML on the Temporal Fusion
Transformer (TFT) and present an initial empirical analysis of forecast accuracy and calibration across three cold-start scenarios: TMAML consistently outperforms or matches a standard ERM-trained TFT, yields better-calibrated forecasts than naive on two of the three scenarios, but does not consistently outperform naive on probabilistic forecast accuracy.
Read the full abstract ▾
Information Theory
Plotkin-type Bound on Function-Correcting Codes for Monomial Mappings
Kanchana Lokshmii Jagatti and B. Sundar Rajan
Function-correcting codes, recently introduced by Lenz et al.\ (2023), provide a framework for the receiver to provide a higher level of protection against channel errors than the level of protection for messages. In this paper, we derive a generalised Plotkin-type bound on the redundancy of FCCs under the Hamming metric for arbitrary functions over finite fields. The proposed bound recovers the classical Plotkin bound for classical error-correcting codes as a special case. We further obtain explicit Plotkin-type bounds for linear functions, Hamming-weight functions, and Hamming-weight threshold functions. In addition, we study FCCs for monomial mappings $f(x)=x^n$ over $\mathbb{F}_{q^k}$, which are widely utilised in coding theory, cryptography, and finite geometry, and derive an explicit Plotkin-type redundancy bound.
Read the full abstract ▾
Multiagent Systems
Nonlinear Density-Driven Optimal Control (D2OC) for Multi-Agent Spatial Coverage via Sequential Convex Programming
Julian Martinez and Kooktae Lee
This paper presents a nonlinear extension of Density-Driven Optimal Control (D2OC) for multi-
agent spatial coverage with prescribed density distributions. Rather than assigning individual target locations, D2OC drives the collective spatial distribution of
agents toward a desired density through a Wasserstein-based objective. We extend this framework to multi-step finite-horizon control for discrete-time control-affine nonlinear systems using sequential convex programming. At each control update, the nonlinear dynamics are locally linearized over the prediction horizon, yielding a strictly convex quadratic program that preserves the Wasserstein barycentric structure while directly incorporating input constraints. We further characterize the effect of constrained control deviations and nonlinear Taylor remainders on the accuracy of the local linear prediction, establishing an explicit finite-horizon error bound and a two-step specialization for receding-horizon implementation. The resulting method retains the decentralized, distribution-driven nature of D2OC while providing a computationally efficient optimization procedure for nonlinear multi-
agent systems. Simulations with unicycle and quadrotor teams show coverage performance comparable to nonlinear model predictive control, while substantially reducing computation time. These results demonstrate a tractable and theoretically characterized framework for density-driven spatial coverage under nonlinear dynamics.
Read the full abstract ▾
Artificial Intelligence
Label Agreement Does Not Measure Authorization
Amir Sabbaghziarani, Bradley Thomas Baker, Theodore J. LaGrow et al. (+1 more)
Many groups now delegate label ontology and metadata harmonization to agentic LLM pipelines. We built one and audited it. Our aggregate scores looked healthy, but the pipeline kept failing in ways they did not show, so we set out to find what they hid. Label agreement asks whether a proposed label matches a reference. It does not ask whether the
agent was entitled to propose it, whether the output was complete enough to act on, or whether the label moved when the evidence moved. We measured those three separately on COBRE and FBIRN, two schizophrenia and control neuroimaging cohorts from different consortia, and they come apart, from label agreement and from each other. Showing the
agent an upstream proposal barely moves label agreement, 0.857 to 0.870, while agreement on the chosen action doubles, 0.409 to 0.830. Output that parses as JSON still drops a required field on 10% of one model's cases and 33% of the other's. And an
agent that replays its first answer scores perfectly on original cases and zero once we change the evidence that decides them. Downstream, a row-order error that none of these metrics reports erases most of the diagnostic signal. So we measure these properties apart, pair each with a control, and gate commitment on the result, which makes failures visible and easy to route to a person. None of this prevents failure. Our reference labels are rule-derived, so agreement with them means consistency, not correctness. Code is available at https://github.com/amir-sbg/Label-Agreement-Does-Not-Measure-Authorization.
Read the full abstract ▾
All authors Amir Sabbaghziarani, Bradley Thomas Baker, Theodore J. LaGrow, Sergey Plis
Cryptography and Security
Quantum Machine Learning Protection of Military Quantum Key Distribution Against Cryptographically Camouflaged Attacks
Muhammad Shaheer Bin Junaid
Quantum key distribution proves its protocol secure and says nothing about the hardware beneath it, so military and government operators fielding it for command-and-control keys monitor the channel for implementation attacks, and that monitoring has a blind spot. An adversary with a kleptographic foothold in the generator of a public per-block value \(x=g^v \pmod p\) can hide attacked blocks in honest noise, gating them on a predicate of its discrete logarithm, making detection a discrete logarithm problem that defeats every efficient classical monitor yet yields to a
quantum kernel recovering \(v\) through Shor's
algorithm. I formalise these cryptographically camouflaged attacks, reduce their hardness to an established learning separation, prove a single-frequency fidelity kernel cannot represent an interval predicate, and test them on Ghillie, a decoy-state BB84 simulator with a positive key rate to 142 km. From 10- to 14-bit groups over two seeds, a classical monitor reads 0.458 to 0.516 on camouflaged attacks while the
quantum kernel reads 1.000, and both catch overt attacks above 0.99. Finite-precision recovery under depolarising noise and a hardened predicate lower the
quantum result to 0.916 through 0.983 with the classical monitor at chance, and a feasibility probe on IBM Heron processors tracks the exact kernel within 0.034. A defender can therefore discard precisely the compromised key material, although the advantage is asymptotic, awaits fault tolerance, and holds only when the feature map matches the adversary's predicate, since a low-frequency map reads 0.545 on a residue pattern and 0.982 once aligned.
Read the full abstract ▾
Artificial Intelligence
Decide, Ask, or Defer: Clinical LLMs under Incomplete Evidence
Mingzhan Yang and Weili Wu
Clinical LLMs must decide not only what diagnosis to produce, but also whether the available evidence is sufficient for autonomous decision making. Binary DECIDE/ABSTAIN formulations merge distinct non decision states and do not explicitly evaluate information acquisition. We introduce a DECIDE/ASK/DEFER formulation together with a blinded protocol that prevents models from using evidence completeness metadata. We evaluate Qwen, Gemini, and GPT on 200 matched clinical evidence states constructed from DDXPlus. The models show substantial differences in action selection under identical evidence, with disagreement in 137 of 200 states. For Qwen, a matched targeted versus random analysis shows that selected information changes the likelihood of a subsequent autonomous decision more clearly than diagnostic correctness. Its matched DECIDE/ABSTAIN baseline further reveals a safety autonomy tradeoff: the three action policy rescues some erroneous autonomous decisions but also removes some correct autonomous deci sions. These results show that separating information acquisition from clinician deferral exposes behavior that binary abstention hides, without yielding a uniformly improved decision policy.
Read the full abstract ▾
Artificial Intelligence
Autonomous Structuring of Radiology Reports Across Modalities at Archive Scale Using an Open-Weight Large Language Model
Friedrich Puttkammer, Fabian Drexel, Marlene Fritzsche et al. (+19 more)
Purpose: To develop and evaluate an open-weight large
language model (LLM) pipeline that converts an entire archive of free-text radiology reports into structured reports without human oversight. Materials and Methods: In this retrospective study, a pipeline with 150 hierarchically organized templates was developed at one center and tested at a second center on reports from 2010 to 2025. The open-weight model gpt-oss-120B selects the template in three constrained-decoding steps and fills it on one local graphics processing unit. Template selection was scored against expert labels on 914 randomly sampled reports of five modalities, structuring quality on 920 radiography and CT reports corrected field by field by five residents. The pipeline then processed the complete archive of the second center. Proportions are reported with Wilson 95% confidence intervals (CIs). Results: An optimal template set was selected for 74.4% of reports (680 of 914; 95% CI: 71.5%, 77.1%) and an appropriate set for 82.3% (752 of 914; 95% CI: 79.7%, 84.6%), 87.7% for single-region and 54.1% for multi-region reports. Macro semantic textual similarity between output and corrected reference was 0.95 for radiography and 0.97 for CT, residents left 88.7% of 24,638 fields unchanged, and unsupported content was flagged in 1.0% and 1.5% of reports. Of 2,186,982 archive reports, 96.5% received structured output, 2,401,544 structured reports, at 1,258 reports per hour on one graphics processing unit. Conclusion: An open-weight LLM pipeline structured a complete multimodality report archive without human oversight with high content fidelity. Multi-region reports remained the main source of template errors.
Read the full abstract ▾
All authors Friedrich Puttkammer, Fabian Drexel, Marlene Fritzsche, Era Stambollxhiu, Miriam Kumpf, Lena Schmitzer, Lea Schumann, Lina Xu, Johannes Moll, Jannik Lübberstedt, Zeineb Ben Chaaben, Anirudh Narayanan, Hartmut Häntze, Renato Cuocolo, Antonios Billis, Alexander Löser, Jawed Nawabi, Marcus R. Makowski, Cosmin I. Bercea, Shahrooz Faghihroohi, Lisa C. Adams, Keno K. Bressem
Robotics
DreamFormer: Dream Imitation with a Transformer World Model for Language-Conditioned Robotic Manipulation
Mostafa Kotb, Cornelius Weber, Muhammad Burhan Hafez et al. (+1 more)
We introduce DreamFormer, a model-based
agent that acquires
language-conditioned, multi-task skills by imitating expert demonstrations within the latent imagination of a learned world model. DreamFormer first learns a task-agnostic
Transformer world model from unstructured play data, then acquires task-specific behaviors by optimizing an intrinsic reward that aligns
agent-generated rollouts with expert demonstrations in latent space. Since the policy is trained on-policy inside imagination, it is exposed to its own errors during training, mitigating the
covariate shift inherent to offline behavioral cloning. To make long-horizon imagination affordable, DreamFormer encodes a high-resolution multi-view robotic observation into a single input
token, avoiding both spatial downsampling and the multi-
token representations used by prior
Transformer world models. On the long-horizon CALVIN
benchmark, DreamFormer outperforms LUMOS, the comparable model-based
agent, on single-environment evaluation (2.52 vs 2.34 average tasks completed per chain of five) while keeping imagination rollouts tractable. Against HULC, the behavior cloning baseline, it nearly doubles performance on zero-shot transfer to an unseen environment (1.30 vs 0.67), indicating that dynamics learned by the world model transfer more readily than a directly cloned policy. This is consistent with the role attributed to internal models in biological
agents, where a model of environment dynamics supports behavior in situations not previously encountered.
Read the full abstract ▾
All authors Mostafa Kotb, Cornelius Weber, Muhammad Burhan Hafez, Stefan Wermter
Artificial Intelligence
Action-Consequence Alignment for Reliable Planning and Self-Improving in Latent World Models
Jinping Wang1, Zhiqiang Gao, Xiantong Zhen et al. (+1 more)
Latent world models learn to predict observed transitions, yet low prediction error alone does not guarantee reliable planning. Inspired by self tickling experiments in neuroscience showing that disrupting motor sensory correspondence increases prediction mismatch, we examine whether learned world models preserve an analogous action consequence correspondence.The results show nearby alternatives can receive lower prediction errors despite producing physical outcomes farther from the recorded target. With that future treated as a goal, this reveals a concrete prediction planning mismatch: the model assigns a lower cost to an action that achieves the target less accurately. To mitigate this gap, we introduce Action Consequence
Alignment (ACA), a training objective that complements forward prediction by penalizing the prediction error advantage of locally searched alternatives over factual actions without additional model components or environment interactions during training. The same principle can also guide additional data collection for self improvement. We demonstrate that across diverse environments and evaluation settings, ACA improves planning performance and reduces real goal error, while ACA guided data collection outperforms random local sampling. These results support action consequence
alignment as a practical principle for bridging predictive learning and reliable planning.
Read the full abstract ▾
All authors Jinping Wang1, Zhiqiang Gao, Xiantong Zhen, Ling Shao
Computer Vision
FASTER: Fast Adjoint Stochastic Transport for Endpoint Refinement in Reward-Guided Image Editing
Yimiao Zhou, Zejia Zhong, Jingya Wang et al. (+1 more)
Reward-guided image editing at test time seeks to improve a specified reward while preserving source content and visual plausibility. Many existing approaches optimize candidates through pretrained generation processes, making repeated adjustment depend on costly large-model execution and, in some cases, backbone backpropagation. We develop a theoretical framework that jointly accounts for reward, source preservation, and pretrained-prior preferences, allowing the desired output distribution to be specified separately from the dynamics used to realize it. Based on this framework, we introduce FASTER, which trains a small network for each source and objective to perform inexpensive editing, while pretrained and reward models provide feedback on candidate outputs. By reusing each candidate and its feedback across multiple small-network updates, FASTER reduces repeated sampling and supervision queries without placing the pretrained generative backbone inside the inner optimization loop. On SD3, FASTER leads all four target metrics and several validation metrics among the evaluated methods. Compared with the evaluated baseline that optimizes controls along pretrained generation trajectories, FASTER achieves editing-time speedups of up to \({6.91\times}\) on Stable
Diffusion 3 and \({24.14\times}\) on Stable
Diffusion 1.5.
Read the full abstract ▾
All authors Yimiao Zhou, Zejia Zhong, Jingya Wang, Ye Shi
Machine Learning
PhaseGate: Phase-Aware CPU Retrieval Scheduling for On-Device LLMs on Unified Memory
Seoyoon Yum and Sehoon Kim
On-device assistants run GPU-based LLM inference alongside CPU
retrieval on unified-
memory systems. Under a saturated local-
retrieval workload, four concurrent
retrieval workers raise 95th-percentile (p95) decode latency by 60-61% on two M4 systems, whereas prefill latency rises by only 5.7-6.9%. We study LLM phase as an admission signal for independent CPU
retrieval under controlled LLM workloads. PHASEGATE calibrates separate concurrency limits for prefill and decode, selecting four and one on our base-M4 configuration. Under a backlogged queue, it achieves 2.0 times the aggregate
retrieval throughput of the best tested feasible fixed policy, with both p95 LLM latency metrics within 1.25 times their no-
retrieval baselines in all seven held-out runs. A phase-blind control, TimeGate, uses the same two limits on a calibration-derived schedule without observing LLM phase. It achieves similar
retrieval throughput but violates the output-
token latency limit in every run. M2 and M2 Pro Mac minis reproduce the policy ordering, while output-length sweeps show that the advantage narrows as decode occupies more of each request.
Read the full abstract ▾
Robotics
WasserMan: Benchmark for Underwater Manipulation Policy Learning
Danil Belov, Artem Erkhov, Sergei Parsegov et al. (+1 more)
Underwater contact manipulation couples visual decisions with vehicle stabilization and mechanism interaction. To our knowledge, WasserMan is the first multi-task simulation
benchmark focused on visuomotor policy learning for manipulating articulated
underwater mechanisms. The suite supports demonstration-based policy learning, alternative vehicle–arm platforms and bimanual manipulation. We compare ACT,
diffusion policies and behavioral cloning under matched sample budgets, and evaluate adaptation of a pretrained SmolVLA baseline. Fixed-policy studies vary water appearance, lighting, contact parameters, actuator delay, arm properties and mechanism pose. RGB feedback controls and matched observation-history and expert-trajectory ablations examine observation and training choices. Expert references establish task feasibility under the same physical success tests. The experiments reveal task-dependent visual transfer and pose sensitivity, while contact, tracking and actuator measurements distinguish execution responses that completion alone hides. Complete expert candidate streams make the initial states associated with demonstration selection inspectable. Versioned code, demonstrations, checkpoints and per-episode physical evidence support reproducible comparisons within the simulation.
Read the full abstract ▾
All authors Danil Belov, Artem Erkhov, Sergei Parsegov, Pavel Osinenko
Numerical Analysis
Pressure Monotonicity for a Rotation-Based Biot Discretization with Circumcentric Mass Lumping
Huipeng Gu, Mingchao Cai and Jingzhi Li
We establish cellwise pressure monotonicity for a mixed finite element discretization of the rotation-based Biot system. The spatial discretization is based on compatible discrete de Rham complexes, using standard lowest-order elements for rotation, displacement, Darcy flux, and pressure. For the temporal discretization, we employ the backward Euler method. Exactness of the discrete complexes reduces the mechanics contribution to the pressure Schur complement to a scaled pressure mass
matrix, yielding a positive diagonal mechanics capacity. For the Darcy part, positive circumcentric facet weights yield a weighted
cell graph
Laplacian after flux elimination, while the mass-lumped product remains uniformly equivalent to the standard Darcy product. The diagonal mechanics capacity and the weighted
cell graph
Laplacian make the condensed pressure
matrix Stieltjes. Consequently, the pressure propagator is entrywise nonnegative, which yields a cellwise discrete maximum principle. Numerical experiments confirm the predicted convergence rates and show that the proposed discretization suppresses spurious cellwise pressure oscillations.
Read the full abstract ▾